176 citations · 1.3k across the 25 of their papers we have counts for
26 papers
Searching for iron nanoparticles with a general-purpose Gaussian approximation potential
Richard Jana, Miguel A. Caro
We present a general-purpose machine learning Gaussian approximation potential (GAP) for iron that is applicable to all bulk crystal structures found experimentally under diverse t…
A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces and nanoparticles
Jan Kloppenburg, Livia B. Pártay, Hannes Jónsson +1
A Gaussian approximation machine learning interatomic potential for platinum is presented. It has been trained on DFT data computed for bulk, surfaces and nanostructured platinum,…
Tensor-reduced atomic density representations
James P. Darby, Dávid P. Kovács, Ilyes Batatia +4
Density based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials,…
Machine learning based modeling of disordered elemental semiconductors: understanding the atomic structure of a-Si and a-C
Miguel A. Caro
Disordered elemental semiconductors, most notably a-C and a-Si, are ubiquitous in a myriad of different applications. These exploit their unique mechanical and electronic propertie…
Cluster-based multidimensional scaling embedding tool for data visualization
Patricia Hernández-León, Miguel A. Caro
We present a new technique for visualizing high-dimensional data called cluster MDS (cl-MDS), which addresses a common difficulty of dimensionality reduction methods: preserving bo…
Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine-learning molecular dynamics simulations
Yanzhou Wang, Zheyong Fan, Ping Qian +2
Amorphous silicon (a-Si) is an important thermal-management material and also serves as an ideal playground for studying heat transport in strongly disordered materials. Theoretica…
Reassignment of magic numbers for icosahedral Au clusters: 310, 564, 928 and 1426
Jan Kloppenburg, Andreas Pedersen, Kari Laasonen +2
Icosahedral Au clusters with three and four shells of atoms are found to deviate significantly from the commonly assumed Mackay structures. By introducing additional atoms in the s…
Accurate computational prediction of core-electron binding energies in carbon-based materials: A machine-learning model combining density-functional theory and
Dorothea Golze, Markus Hirvensalo, Patricia Hernández-León +6
We present a quantitatively accurate machine-learning (ML) model for the computational prediction of core-electron binding energies, from which x-ray photoelectron spectroscopy (XP…
Structure and Pore Size Distribution in Nanoporous Carbon
Yanzhou Wang, Zheyong Fan, Ping Qian +2
We study the structural and mechanical properties of nanoporous (NP) carbon materials by extensive atomistic machine-learning (ML) driven molecular dynamics (MD) simulations. To th…
Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C
Heikki Muhli, Xi Chen, Albert P. Bartók +4
We present a comprehensive methodology to enable addition of van der Waals (vdW) corrections to machine learning (ML) atomistic force fields. Using a Gaussian approximation potenti…
Particle Swarm Based Hyper-Parameter Optimization for Machine Learned Interatomic Potentials
Suresh Kondati Natarajan, Miguel A. Caro
Modeling non-empirical and highly flexible interatomic potential energy surfaces (PES) using machine learning (ML) approaches is becoming popular in molecular and materials researc…
Machine learning driven simulated deposition of carbon films: from low-density to diamondlike amorphous carbon
Miguel A. Caro, Gábor Csányi, Tomi Laurila +1
Amorphous carbon (a-C) materials have diverse interesting and useful properties, but the understanding of their atomic-scale structures is still incomplete. Here, we report on exte…
Fully analytic valence force field model for the elastic and inner elastic properties of diamond and zincblende crystals
Daniel S. P. Tanner, Miguel A. Caro, Stefan Schulz +1
Using a valence force field model based on that introduced by Martin, we present three related methods through which we analytically determine valence force field parameters. The m…
Towards the computational experiment
Miguel A. Caro
We give a brief account of the current limitations and possibilities in the field of computational simulation of materials. We then focus on the effect that the emergence of machin…
Optimizing many-body atomic descriptors for enhanced computational performance of machine learning based interatomic potentials
Miguel A. Caro
We explore different ways to simplify the evaluation of the smooth overlap of atomic positions (SOAP) many-body atomic descriptor [Bartók et al., Phys. Rev. B 87, 184115 (2013)]. O…
Hybrid functional study of non-linear elasticity and internal strain in zincblende III-V materials
Daniel S. P. Tanner, Miguel A. Caro, Stefan Schulz +1
We investigate the elastic properties of selected zincblende III-V semiconductors. Using hybrid functional density functional theory we calculate the second and third order elastic…
Growth Mechanism and Origin of High Content in Tetrahedral Amorphous Carbon
Miguel A. Caro, Volker L. Deringer, Jari Koskinen +2
We study the deposition of tetrahedral amorphous carbon (ta-C) films from molecular dynamics simulations based on a machine-learned interatomic potential trained from density-funct…
Parametrization of the Tkatchenko-Scheffler dispersion correction scheme for popular exchange-correlation density functionals: effect on the description of liquid water
Miguel A. Caro
We present a list of optimized damping range parameters to be used with the Tkatchenko-Scheffler van der Waals dispersion-correction scheme [Phys. Rev. Lett. 102, 073005 (200…
Random alloy fluctuations and structural inhomogeneities in -plane InGaN quantum wells: theory of ground and excited electron and hole states
Daniel S. P. Tanner, Miguel A. Caro, Eoin P. O'Reilly +1
We present a detailed theoretical analysis of the electronic structure of -plane InGaN/GaN quantum wells with indium contents varying between 10\% and 25\%. The electronic struc…
Structural, electronic, and optical properties of -plane InGaN/GaN quantum wells: Insights from experiment and atomistic theory
S. Schulz, D. P. Tanner, E. P. O'Reilly +13
In this paper we present a detailed analysis of the structural, electronic, and optical properties of an -plane (In,Ga)N/GaN quantum well structure grown by metal organic vapor…
Origin of non-linear piezoelectricity in III-V semiconductors: Internal strain and bond ionicity from hybrid-functional density functional theory
Miguel A. Caro, Stefan Schulz, Eoin P. O'Reilly
We derive first- and second-order piezoelectric coefficients for the zinc-blende III-V semiconductors, {Al,Ga,In}-{N,P,As,Sb}. The results are obtained within the Heyd-Scuseria-Ern…
Atomistic analysis of the impact of alloy and well-width fluctuations on the electronic and optical properties of InGaN/GaN quantum wells
Stefan Schulz, Miguel A. Caro, Conor Coughlan +1
We present an atomistic description of the electronic and optical properties of N/GaN quantum wells. Our analysis accounts for fluctuations of wel…
Extended scheme for the projection of material tensors of arbitrary symmetry onto a higher symmetry tensor
Miguel A. Caro
I propose a straightforward generalization of the projection scheme for elastic tensors introduced by Moakher and Norris [J. Elasticity 85, 215 (2006)] that takes into account also…
Orbital-Free Density Functional Theory Implementation with the Projector Augmented-Wave Method
J. Lehtomäki, I. Makkonen, M. A. Caro +2
We present a computational scheme for orbital-free density functional theory (OFDFT) that simultaneously provides access to all-electron values and preserves the OFDFT linear scali…